Journal of the Serbian Chemical Society 2016 Volume 81, Issue 8, Pages: 959-970
https://doi.org/10.2298/JSC160108032M
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Modeling the removal of Sunfix Red S3B from aqueous solution by electrocoagulation process using artificial neural network
Manh Ha Bui (Sai Gon university, Department of Environmental Sciences, Ho Chi Minh, Vietnam)
This study presents an application of artificial neural networks (ANNs) to
predict the dye removal efficiency (color and chemical oxygen demand value)
of Electrocoagulation process from Sunfix Red S3B aqueous solution. The
Bayesian regulation algorithm was applied to train the networks with
experimental data including five factors: pH, current density, sulphate
concentration, initial dye concentration (IDC), and electrolysis time. The
predicting performance of the ANN models was validated through the low root
mean square error value (9.844 %), mean absolute percentage error (13.776 %)
and the high determination coefficient value (0.836). Garson, Connection
weight method and neural interpretation diagram were also used to study the
influence of input variables on dye removal efficiency. For decolorization,
the most effective inputs are determined as current density, electrolysis
time and initial pH, while COD removal is found to be strongly affected by
initial dye concentration and sulphate concentration. Through these steps, we
demonstrated ANN’s robustness in modeling and analysis of electrocoagulation
process.
Keywords: color removal, Garson’s algorithm, sensitivity analysis, textile wastewater